Automatic visual inspection of thermoelectric metal pipes

Abstract

This paper presents the main aspects of the design of an image acquisition and processing approach that can be inserted into thermoelectric metal pipe systems and travel inside the pipes to capture images from the inner surface of such pipes for further analysis. After the image capture, a preprocessing is applied based on iris recognition, which transforms the image from a Cartesian coordinate system to a polar coordinate system, which allows a better texture analysis of the internal surface of the pipe. The extracted information is used to train a classifier capable of automatically identifying segments that present some type of corrosion or defects. The experimental results in a dataset of 6150 images using two textural features have shown that the proposed classification approach can achieve accuracy between 96 and 98% in the test set.

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References

  1. 1.

    Obrutsky, L., Renaud, J., Lakhan, R.: Overview of steam generator tube-inspection technology. CINDE J. 35(2), 5–13 (2009)

    Google Scholar 

  2. 2.

    Obrutsky, L., Renaud, J., Lakhan, R.: Steam generator inspections: faster, cheaper and better, are we there yet? In: IV Conferencia Panamericana de END, pp. 1–17 (2007)

  3. 3.

    Malamas, E.N., Petrakis, E.G.M., Zervakis, M., Petit, L., Legat, J.D.: A survey on industrial vision systems, applications and tools. Image Vis. Comput. 21, 171–188 (2003)

    Article  Google Scholar 

  4. 4.

    Ngan, H.Y.T., Pang, G.K.H., Yung, N.H.C.: Automated fabric defect detection: a review. Image Vis. Comput. 29, 442–458 (2011)

    Article  Google Scholar 

  5. 5.

    Mansano, M., Pavesi, L., Oliveira, L., Britto, A., Koerich, A.: Inspection of metallic surfaces using local binary patterns. In: 37th Annual Conference on IEEE Industrial Electronics Society (IECON 2011), pp. 2227–2231. IEEE (2011)

  6. 6.

    Debenest, P., Guarnieri, M., Hirose, S.: Pipetron series-robots for pipe inspection. In: Proceedings of the 3rd International Conference on Applied Robotics for the Power Industry, pp. 1–6 (2014)

  7. 7.

    Maglietta, R., Milella, A., Caccia, M., Bruzzone, G.: A vision-based system for robotic inspection of marine vessels. Signal Image Video Process 12, 471–478 (2018)

    Article  Google Scholar 

  8. 8.

    Sinha, S.K., Fieguth, P.W.: Neuro-fuzzy network for the classification of buried pipe defects. Autom. Constr. 15(1), 73–83 (2006)

    Article  Google Scholar 

  9. 9.

    Huynh, P., Ross, R., Martchenko A., Devlin, J.: Dou-edge evaluation algorithm for automatic thin crack detection in pipelines. In: International Conference on Signal and Image Processing Applications (ICSIPA), pp. 191–196. IEEE (2015)

  10. 10.

    Ogai, H., Bhattacharya, B.: Pipe inspection robots for structural health and condition monitoring. In: Intelligent Systems, Control and Automation: Science and Engineering, pp. 107–122. Springer (2018)

  11. 11.

    Myrans, J., Kapelan, Z., Everson, R.: Automated detection of faults in wastewater pipes from CCTV footage by using random forests. Procedia Eng. 154, 36–41 (2016)

    Article  Google Scholar 

  12. 12.

    Rzhanov, Y.: Photo-mosaicing of images of pipe inner surface. Signal Image Video Process. 7, 865–871 (2013)

    Article  Google Scholar 

  13. 13.

    Kain, V., Roychowdhury, S., Ahmedabadi, P., Barua, D.K.: Flow accelerated corrosion: experience from examination of components from nuclear power plants. Eng. Fail. Anal. 18(8), 2028–2041 (2011)

    Article  Google Scholar 

  14. 14.

    Costa, Y.M., Oliveira, L., Koerich, A.L., Gouyon, F., Martins, J.: Music genre classification using LBP textural features. Signal Process. 92(11), 2723–2737 (2012)

    Article  Google Scholar 

  15. 15.

    Zavaschi, T.H.H., Britto, A.S., Oliveira, L.E.S., Koerich, A.L.: Fusion of feature sets and classifiers for facial expression recognition. Expert Syst Appl 40(2), 646–655 (2013)

    Article  Google Scholar 

  16. 16.

    Brahnam, S., Jain, L.C., Nanni, L., Lumini, A.: Local Binary Patterns: New Variants and Applications, Studies in Computational Intelligence, vol. 506, pp. 1–17, (2014)

  17. 17.

    Costa, Y., Oliveira, L., Koerich, A.L., Gouyon, F.: Music genre recognition using gabor filters and lpq texture descriptors. In: Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications, pp. 67–74. Springer, Berlin (2013)

  18. 18.

    Pontes, J.K., Britto, A.S., Fookes, C., Koerich, A.L.: A flexible hierarchical approach for facial age estimation based on multiple features. Pattern Recognit. 54, 34–51 (2016)

    Article  Google Scholar 

  19. 19.

    Subban, R., Dattatreya, N.S.: Efficient iris recognition using haralick features based extraction and fuzzy particle swarm optimization. Clust. Comput. 21(1), 1–12 (2017)

    Google Scholar 

  20. 20.

    Cavalin, P., Oliveira, L.S., Koerich, A.L., Britto, A.S.: Wood defect detection using grayscale images and an optimized feature set. In: IECON 2006-32nd Annual Conference on IEEE Industrial Electronics, pp. 3408–3412. Springer (2006)

  21. 21.

    Costa, Y.M.G., Oliveira, L.E.S., Koerich, A.L., Gouyon, F.: Comparing textural features for music genre classification. In: The 2012 International Joint Conference on Neural Networks (IJCNN), pp. 1–6. IEEE (2012)

  22. 22.

    Daugman, J.: The importance of being random: statistical principles of iris recognition. Pattern Recognit. 36(2), 279–291 (2003)

    Article  Google Scholar 

  23. 23.

    Llano, E.J., Vázquez, M.S.G., Vargas, J.M.C., Fuentes, L.M.Z., Acosta, A.A.R.: Optimized robust multi-sensor scheme for simultaneous video and image iris recognition. In: Pattern Recognition Letters, vol. 101, pp. 44–51. Elsevier (2018)

  24. 24.

    Araujo, H., Dias, J.M.: An introduction to the log-polar mapping. In: Proceedings II Workshop on Cybernetic Vision, pp. 139–144 (1997)

  25. 25.

    Ojansivu, V., Heikkilä, J.: Blur insensitive texture classification using local phase quantization. In: 3rd International Conference on Image and Signal Processing, vol. 101, pp. 236–243. Springer (2008)

  26. 26.

    Haralick, R.M., Dinstein, I., Shanmugam, K.: Textural features for image classification. IEEE Trans. Syst. Man Cybern. 3(6), 610–621 (1973)

    Article  Google Scholar 

  27. 27.

    Martins, J.G., Oliveira, L.S., Sabourin, R.: Combining textural descriptors for forest species recognition. In: IECON 2012-38th Annual Conference on IEEE Industrial Electronics Society, pp. 1483–1488. IEEE (2012)

  28. 28.

    Fisher, R.A.: The use of multiple measurements in taxonomic problems. Ann. Eugen. 7(7), 179–188 (1936)

    Article  Google Scholar 

  29. 29.

    Yadav, A.R., Anand, R.S., Dewal, M.L., Gupta, S.: Multiresolution local binary pattern variants based texture feature extraction techniques for efficient classification of microscopic images of hardwood species. Appl. Soft Comput. J. 32, 101–112 (2015)

    Article  Google Scholar 

  30. 30.

    Kadrolkar, A., SupIV, F.: Intent recognition of torso motion using wavelet transform feature extraction and linear discriminant analysis ensemble classification. Biomed. Signal Process. Control 38, 250–264 (2017)

    Article  Google Scholar 

  31. 31.

    Ding, Y., Pardon, M., Duan, J., Agostini, A., Faas, H., Ward, W., Auer, D., Easton, F., Bai, L.: Novel methods for microglia segmentation, feature extraction, and classification. IEEE/ACM Trans. Comput. Biol. Bioinform. 14(6), 1366–1377 (2016)

    Article  Google Scholar 

  32. 32.

    Cortes, C., Vapnik, V.: Support-vector networks. In: Machine Learning, pp. 273–297 (1995)

  33. 33.

    Ledoit, O., Wolf, M.: Honey, I shrunk the sample covariance matrix. Eng. Fail. Anal. 30(4), 106–119 (2004)

    Google Scholar 

  34. 34.

    Yang, D., Subramanian, G., Duan, J., Gao, S., Bai, L., Chandramohanadas, R., Ai, Y.: A portable image-based cytometer for rapid malaria detection and quantification. PLoS ONE 12, 1–18 (2017)

    Google Scholar 

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Acknowledgements

The authors of this work acknowledge the ANEEL for the Research and Development program, the Neonergia Group, for the project funding and the LACTEC for the infra structure and support.

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Correspondence to Daniel Vriesman.

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Vriesman, D., Britto, A.S., Zimmer, A. et al. Automatic visual inspection of thermoelectric metal pipes. SIViP 13, 975–983 (2019). https://doi.org/10.1007/s11760-019-01435-2

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Keywords

  • Visual inspection
  • Texture
  • Fusion of features
  • Automatic inspection